This paper presents a massive multiple-inputmultiple-output (mMIMO) system that utilizes spectrally efficient frequency division multiplexing (SEFDM) and integrates deep learning (DL) for improved channel estimation in the mMIMO-SEFDM. In contrast to the literature, this study utilizes the estimated channel feedback to dynamically adapt the SEFDM signal characteristics at the transmitter, which improves the system's agility. This enables the selection of the SEFDM signal's compression value and modulation order depending on the channel conditions, thus enhancing the symbol error rate (SER). The simulation findings demonstrate that using DLbased channel estimation and adaptive parameter selection for SEFDM leads to a better symbol error rate and improved spectral efficiency compared to orthogonal frequency division multiplexing (OFDM).

In this study, a deep neural network (DNN) for a massive multiple-input-multiple-output (mMIMO) nonorthogonal multiple access (NOMA) system’s channel estimation and detection is presented. The proposed network applies deep learning (DL) to a discrete wavelet transform (DWT) based orthogonal frequency-division-multiplexing (OFDM) to reduce the noise and inter-channel interference (ICI) while maintaining compatibility with the existing networks. The transmission overhead is reduced because the presented network is trained to estimate and detect symbols with and without pilot signals. The results of this study demonstrate that our provided DL-based successive interference cancellation (SIC) method performs better than the conventional SIC-based signal detection in the mMIMONOMA network. The suggested wavelet-based mMIMO-NOMA is also compared to the traditional fast-Fourier-transform (FFT) based mMIMO-NOMA, particularly in terms of symbol error rate (SER).

In this paper, a novel image-based deep learning (DL) approach for channel estimation for future wireless communications is proposed. The time-frequency response of fast fading wireless channel is represented as a 2D image to estimate the unknown values of the channel response using known values at the pilot locations. With given images, both image superresolution (SR) and image restoration (IR), termed as super resolution and denoising network (SRDnN), are combined to estimate wireless channel. To show the effectiveness, the proposed SRDnN is applied to massive multiple-input multiple output (mMIMO) with non-orthogonal multiple access (NOMA). The enhanced performances of SRDnN are quantified in terms of mean square error (MSE) and symbol error rate (SER). In addition, the influence of pilot numbers on SRDnN performance for next generation mMIMO-NOMA networks is presented. The simulation results demonstrate that SRDnN is comparable to the level of maximum likelihood (ML) detection for both with and without complete channel state information (CSI) at the receiver with less number of pilots.